Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment
Sini Anna Alex1, Anita Kanavalli2
1Mrs. Sini Anna Alex*, Assistant Professor in Computer Science Department of M S Ramaiah Institute of Technology.
2Dr. Anita Kanavalli, Head of Computer Science Department of M S Ramaiah Institute of Technology
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3521-3526 | Volume-8 Issue-12, October 2019. | Retrieval Number: L26221081219/2019©BEIESP | DOI: 10.35940/ijitee.L2622.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture is one of the biggest fields to improve the economic rate of the country. Crop yield prediction is a new emerging idea in agriculture. There are several challenges of crops yield prediction in the field of precision agriculture are (i). Obtain minimized production due to climate change; (ii). Lead to different diseases; (iii). Availability of Water; (iv). No awareness of fertilizers and crop features; (v). Climate change; (vi). Unexpected weather events.Other loss factors in the agriculture are lowly seed quality, unplanned irrigation and exploitation of insecticides and fertilizers. The main aim of this research is to design the effective crop yield production and health risk analysis model by big data analytics model. Hence in this research our focus is on optimizing the significant parameters such as rainfall, temperature and fertilizers rate to obtain the P-values for testing the crop and also analyze the human health safety (farmers and suppliers) due to the dynamic change of environment and also soil nutrients. Big data analytics is the feasible platform to test and measure the crop grow in the particular agriculture field. It helps in climate, weather events prediction and also it is used to compute the sufficient resources for crop cultivation.
Keywords: Clustering, Fertilizer Planning, Data Analytics, Prediction
Scope of the Article: Clustering